Open Access Research article

Genomic testing to determine drug response: measuring preferences of the public and patients using Discrete Choice Experiment (DCE)

Mehdi Najafzadeh1, Karissa M Johnston3, Stuart J Peacock36, Joseph M Connors36, Marco A Marra4, Larry D Lynd25 and Carlo A Marra25*

Author Affiliations

1 Department of Medicine, Harvard Medical School, Boston, MA, USA

2 Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, Canada

3 British Columbia Cancer Agency, Vancouver, BC, Canada

4 Genome Sciences Centre, Vancouver, BC, Canada

5 Centre for Health Evaluation and Outcome Sciences (CHEOS), St. Paul’s Hospital, 1081 Burrard Street, Vancouver, BC, Canada

6 Faculty of Medicine, University of British Columbia, Vancouver, Canada

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BMC Health Services Research 2013, 13:454  doi:10.1186/1472-6963-13-454

Published: 31 October 2013

Abstract

Background

The extent to which a genomic test will be used in practice is affected by factors such as ability of the test to correctly predict response to treatment (i.e. sensitivity and specificity of the test), invasiveness of the testing procedure, test cost, and the probability and severity of side effects associated with treatment.

Methods

Using discrete choice experimentation (DCE), we elicited preferences of the public (Sample 1, N = 533 and Sample 2, N = 525) and cancer patients (Sample 3, N = 38) for different attributes of a hypothetical genomic test for guiding cancer treatment. Samples 1 and 3 considered the test/treatment in the context of an aggressive curable cancer (scenario A) while the scenario for sample 2 was based on a non-aggressive incurable cancer (scenario B).

Results

In aggressive curable cancer (scenario A), everything else being equal, the odds ratio (OR) of choosing a test with 95% sensitivity was 1.41 (versus a test with 50% sensitivity) and willingness to pay (WTP) was $1331, on average, for this amount of improvement in test sensitivity. In this scenario, the OR of choosing a test with 95% specificity was 1.24 times that of a test with 50% specificity (WTP = $827). In non-aggressive incurable cancer (scenario B), the OR of choosing a test with 95% sensitivity was 1.65 (WTP = $1344), and the OR of choosing a test with 95% specificity was 1.50 (WTP = $1080). Reducing severity of treatment side effects from severe to mild was associated with large ORs in both scenarios (OR = 2.10 and 2.24 in scenario A and B, respectively). In contrast, patients had a very large preference for 95% sensitivity of the test (OR = 5.23).

Conclusion

The type and prognosis of cancer affected preferences for genomically-guided treatment. In aggressive curable cancer, individuals emphasized more on the sensitivity rather than the specificity of the test. In contrast, for a non-aggressive incurable cancer, individuals put similar emphasis on sensitivity and specificity of the test. While the public expressed strong preference toward lowering severity of side effects, improving sensitivity of the test had by far the largest influence on patients’ decision to use genomic testing.

Keywords:
Pharmacogenomics; Genomic medicine; Personalized medicine; Genetic testing; Discrete choice experiment; Conjoint analysis; Preference elicitation; Cancer treatment